A novel length-based empirical estimation method of spawning potential ratio (SPR), and tests of its performance, for small-scale, data-poor fisheries

Abstract

The spawning potential ratio (SPR) is a well-established biological reference point, and estimates of SPR could be used to inform management decisions for data-poor fisheries. Simulations were used to investigate the utility of the length-based model (LB-SPR) developed in Hordyk et al. (2015). Some explorations of the life history ratios to describe length composition, spawning-per-recruit, and the spawning potential ratio. ICES Journal of Marine Science, 72: 204–216.) to estimate the SPR of a stock directly from the size composition of the catch. This was done by (i) testing some of the main assumptions of the LB-SPR model, including recruitment variability and dome-shaped selectivity, (ii) examining the sensitivity of the model to error in the input parameters, and (iii) completing an initial empirical test for the LB-SPR model by applying it to data from a well-studied species. The method uses maximum likelihood methods to find the values of relative fishing mortality (F/M) and selectivity-at-length that minimize the difference between the observed and the expected length composition of the catch, and calculates the resulting SPR. When parameterized with the correct input parameters, the LB-SPR model returned accurate estimates of F/M and SPR. With high variability in annual recruitment, the estimates of SPR became increasingly unreliable. The usefulness of the LB-SPR method was tested empirically by comparing the results predicted by the method with those for a well-described species with known length and age composition data. The results from this comparison suggest that the LB-SPR method has potential to provide a tool for the cost-effective assessment of data-poor fisheries. However, the model is sensitive to non-equilibrium dynamics, and requires accurate estimates of the three parameters (M/k, L∞, and CVL∞). Care must be taken to evaluate the validity of the assumptions and the biological parameters when the model is applied to data-poor fisheries.

An evaluation of an iterative harvest strategy for data-poor fisheries using the length-based spawning potential ratio assessment methodology

“…Sensitivity testing of the LB-SPR assessment model identified that the method is 380 sensitive to violations of the equilibrium assumption, with increased recruitment variability 381 resulting in increased error in the estimates of SPR (Hordyk et al, 2014b). The iterative 382 HCR uses estimates of the current SPR from the LB-SPR method, and any error or bias in 383 these estimates are likely to impact the performance of the iterative harvest strategy.…”

“…The LB-SPR model estimates the SPR by comparing the observed length structure to 103 the expected unfished length composition and has the advantage of requiring only minimal 104 data: i.e., a representative length sample of the stock and basic life history information for the 105 species (Hordyk et al, 2014a(Hordyk et al, , 2014b. Information on the length structure of an exploited 106 stock is often one of the cheapest and easiest data sets to collect (Quinn and Deriso, 1999).…”

The Analysis of Tuna Resource Utilization Level With Spawning Potential Ratio Method in Sendangbiru Waters

“…Pendekatan ini menggunakan data frekuensi panjang suatu ikan sebagai input dan digunakan pada perikanan dengan data yang masih sedikit atau kurang memadai. Beverton (1992) dalam Hordyk et al (2014) terdapat beberapa keuntungan atau kelebihan pendekatan LB-SPR yakni: (1) data panjang lebih mudah dan lebih murah untuk dikumpulkan disbandingkan dengan data usia suatu spesies ikan, (2) tidak terdapat banyak variasi dalam rasio kematian dan partumbuhan dalam satu spesies ikan Model pendekatan berbasis LB-SPR merupakan metode yang berdasarkan keseimbangan dengan menggunakan asumsi khusus bila diterapkan pada perikanan yang memiliki data sangat terbatas. Asumsiasumsi tersebut meliputi: (1) selektivitas alat tangkap, (2) pertumbuhan yang dijelaskan oleh persamaan von Bertalanffy, (3) parameter komposisi panjang yang digunakan berasal dari ikan jantan dan ikan betina karena keduanya memiliki peluang yang sama untuk ditangkap sehingga kurva pertumbuhannya bersifat tunggal/ dapat digunakan untuk menggambarkan pertumbuhan kedua jenis kelamin ikan, (4) ukuran panjang pada umur tertentu terdistribusi secara normal, (5) tingkat kematian alami konstan, (6) tingkat pertumbuhan konstan pada berbagai kohort dalam satu stok (Prince et al, 2015).…”

A simple length-structured model based on life history ratios and incorporating size-dependent selectivity: application to spawning potential ratios for data-poor stocks

“…This approach would essentially be identical to the age-structured LBSPR model 181 (Hordyk et al , 2015c, which assumed individuals were normally distributed around a 182 mean length-at-age. However, a shortcoming of this approach is that it fails to adequately 183 account for the cumulative effects of size-based fishing mortality on the size structure of the 184 stock, although admittedly some form of truncation could be adopted within this simple 185 statistical approach.…”

“…We then compared the estimates of the newly developed GTG LB-SPR model, 88 and the original LB-SPR model (Hordyk et al , 2015c, by applying the two methods 89 to both simulated and empirical data sets. Similar to the LB-SPR model, the GTG LB-SPR 90 model developed here assumes known life history parameters, and uses an equilibrium per-91 recruit model to estimate the relative fishing mortality, selectivity-at-length, and the 92 spawning potential ratio, from representative catch-at-length data.…”